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GPU Cloud
Distributed Training
Distributed training splits AI model training across multiple GPUs or nodes for faster convergence.
Definition
Distributed training is a technique for training large AI models across multiple GPUs or nodes, enabling training of models too large for a single GPU. The main approaches are: (1) Data Parallelism — each GPU processes different batches, (2) Tensor Parallelism — model layers split across GPUs, (3) Pipeline Parallelism — model split into stages. Frameworks like Megatron-LM, DeepSpeed, and PyTorch FSDP implement these strategies. Harch Corp's GPU clusters support all distributed training paradigms with 400Gb/s InfiniBand networking.
Related Keywords
distributed trainingdata parallelismtensor parallelismpipeline parallelismdeepspeed
Related Terms
AI Training
AI training is the process of optimizing model parameters using labeled data and compute resources.
GPU Cluster
A GPU cluster is a network of GPUs working together for distributed AI training and HPC workloads.
InfiniBand
InfiniBand is a high-speed networking standard used in HPC and AI GPU clusters.